RAG Assistant

Overview

A sophisticated chatbot system leveraging Retrieval-Augmented Generation (RAG) to provide accurate, context-aware responses from document repositories. The system combines modern AI technologies with efficient vector storage for enhanced information retrieval and natural conversations.

Key Features

• Real-time Document Processing: Efficiently processes and indexes various document formats (PDF, DOCX, TXT, JSON)
• Intelligent Retrieval: Uses Weaviate vector database for semantic search and relevant context fetching
• Context-Aware Responses: Generates natural, contextually accurate responses using OpenAI's GPT models
• Scalable Architecture: Containerized with Docker for easy deployment and scaling
• WebSocket Integration: Supports real-time bidirectional communication for smooth chat experience

Technical Implementation

• Built with FastAPI for high-performance async API handling
• Implements LangChain for streamlined LLM operations
• Uses Redis for efficient caching and session management
• Features comprehensive error handling and fallback responses
• Includes robust testing suite for multi-client scenarios

Impact

This system demonstrates advanced capabilities in document understanding and natural language processing, making it ideal for applications in customer service, documentation assistance, and knowledge base interaction.